Accidents and crimes have been increased with the complexity and diversity of modern society. Thus, safety and surveillance have been increasingly demanded. In order to satisfy the demand of safety and surveillance, unmanned monitoring systems, etc. have been developed to monitor principal facilities and keep public security in crime-ridden districts.
As a practical and economic alternative, such unmanned monitoring systems have been developed into intelligent monitoring systems, which detect and track moving objects, beyond the development of Digital Video Recorders (DVRs). Such intelligent monitoring system technology is to detect and distinguish moving objects from image sequences. In order to realize such intelligent monitoring system technology, there is required technology for automatically distinguishing and tracking objects in the environments of multiple monitoring cameras.
In the early algorithms for tracking objects, several cameras track objects using camera calibration and overlapping Fields of View (FOV). As one of such algorithms, there is a method of constituting cameras using a compensated camera to overlap viewers with one another and then calculating handovers of tracked objects.
Another method is to track objects in cameras of which viewers do not overlap with one another. In other words, in this method, the viewers do not overlap with one another in the cameras in order to match transition times of the objects with shapes of the objects. However, in this method, the transition times of the objects must be used, and moving methods of people must be pre-informed.
There is another method of describing motion patterns of objects from two cameras using a stochastic transition matrix. This method requires learned data in order to confirm coherences among the cameras.
Besides the above-described method, there is a method of constituting a re-entry period and then using objects that are observed within a given time. In this method, the re-entry period is expressed as a histogram using the observed objects, and links among cameras are detected based on the histogram.
There is another method of modeling posterior probability distributions and color variations of space-time links among cameras using progressive learning. This method is to verify coherences of objects in multiple cameras that do not overlap, based on a progressive learning process for similarities among colors. Here, the similarities among the colors are determined using a Consensus-Color Conversion of Munsell (CCCM) color space. Links among the multiple cameras are determined after the determinations of the similarities among the colors.
In the above-method, entries and re-entries of objects are calculated as conditional transition probabilities within a given time to determine the links among the multiple cameras. Here, in order to further accurately determine the links, blocks within screens of the multiple cameras are divided into maximally small sizes to remove unnecessary links and sort out effective links. After the links are sorted out, the posterior probability distributions are used to further accurately determine the coherences of objects that are observed among links.
Another method is to confirm coherences of objects using relations between FOV lines in the environment in which camera viewers do not overlap with one another in real-time situations. In this method, the FOV lines of the cameras, which do not overlap with one another, extend to form virtual lines, and then minimum distances from the virtual lines to estimated positions of objects are calculated in order to determine whether the objects are the same objects.
In the above-described object tracking methods, cameras are constituted using various methods. Here, the cameras may be classified into single cameras and multiple cameras. Also, the multiple cameras may be classified into overlapping multiple cameras and non-overlapping multiple cameras according to whether images overlap with one another. The overlapping multiple cameras overlap camera images, while the non-overlapping multiple cameras do not overlap camera images.
The single cameras are mainly used to perform defined functions of specific spatial areas like over-speeding car regulating cameras. The multiple cameras are used to keep and monitor safety in indoor and outdoor wide areas. The overlapping multiple cameras are used to allow two or more cameras to share a predetermined area so as to minutely analyze images or used to produce 3-dimensional (3-D) images through stereo vision technology.
However, the non-overlapping multiple cameras are mainly used in monitoring systems for wide areas due to installation and maintenance cost. External identifiers of objects and haunting time intervals of the objects must be considered to track the objects in the non-overlapping multiple cameras.